| 朱全,纪萍,郭家伟.基于改进k-means聚类算法的车轮踏面损伤检测研究[J].西昌学院学报（自然科学版）,2023,37(3):52-58. |
| 基于改进k-means聚类算法的车轮踏面损伤检测研究 |
| Study on Wheel Tread Damage Detection Based on Improved k-means Clustering Algorithm |
| 投稿时间：2023-03-22 修订日期：2023-07-25 |
| DOI：10.16104/j.issn.1673-1891.2023.03.009 |
| 中文关键词: 车轮踏面 动态检测 k-means聚类算法 机器视觉 |
| 英文关键词: wheel tread dynamic detection k-means clustering algorithm machine vision |
| 基金项目:安徽省高校自然科学研究重点项目（KJ2021A1235）。 |
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| 中文摘要: |
| 为提高列车车轮踏面检测效率，设计了一套基于机器视觉的车轮踏面动态检测系统，分析了k-means聚类算法，通过加权欧式距离对该算法进行改进，利用聚类法具有保持最大相似性的特性，将基于加权欧式距离的k-means聚类算法用于机器视觉的图像处理。先对原始图像作图像增强、图像灰度化等预处理，再以特征聚类思想对图像作阈值分割，使图像中的各部分特征更加突出。图像处理结果显示，基于加权欧式距离k-means聚类算法的车轮踏面损伤视觉检测系统可以有效地检测出踏面损伤。 |
| 英文摘要: |
| In order to improve the efficiency of train wheel tread detection, a dynamic wheel tread detection system based on machine vision was designed, and k-means clustering algorithm was analyzed. The algorithm was improved by weighted Euclidean distance. With the clustering method’s characteristics of maintaining maximum similarity, k-means clustering algorithm based on weighted Euclidean distance was used for image processing in machine vision. First, the original image was preprocessed by image enhancement and gray scale, and then the image was threshold segmented using feature clustering to make the features of each part of the image more prominent. The image processing results show that the visual detection system of wheel tread damage based on weighted Euclidean distance k-means clustering algorithm can effectively detect tread damage. |
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